Supervised risk predictor of central gland lesions in prostate cancer using ¹H MR spectroscopic imaging with gradient offset‐independent adiabaticity pulses

Abstract

Background Due to the histological heterogeneity of the central gland, accurate detection of central gland prostate cancer remains a challenge. Purpose To evaluate the efficacy of in vivo 3D 1H MR spectroscopic imaging (3D 1H MRSI) with a semi‐localized adiabatic selective refocusing (sLASER) sequence and gradient‐modulated offset‐independent adiabatic (GOIA) pulses for detection of central gland prostate cancer. Additionally four risk models were developed to differentiate 1) normal vs. cancer, 2) low‐ vs. high‐risk cancer, 3) low‐ vs. intermediate‐risk cancer, and 4) intermediate‐ vs. high‐risk cancer voxels. Study Type Prospective. Subjects Thirty‐six patients with biopsy‐proven central gland prostate cancer. Field Strength/Sequence 3T MRI / 3D 1H MRSI using GOIA‐sLASER. Assessment Cancer and normal regions of interest (ROIs) were selected by an experienced radiologist and 1H MRSI voxels were placed within the ROIs to calculate seven metabolite signal ratios. Voxels were split into two subsets, 80% for model training and 20% for testing. Statistical Tests Four support vector machine (SVM) models were built using the training dataset. The accuracy, sensitivity, and specificity for each model were calculated for the testing dataset. Results High‐quality MR spectra were obtained for the whole central gland of the prostate. The normal vs. cancer diagnostic model achieved the highest predictive performance with an accuracy, sensitivity, and specificity of 96.2%, 95.8%, and 93.1%, respectively. The accuracy, sensitivity, and specificity of the low‐ vs. high‐risk cancer and low‐ vs. intermediate‐risk cancer models were 82.5%, 89.2%, 70.2%, and 73.0%, 84.7%, 60.8%, respectively. The intermediate‐ vs. high‐risk cancer model yielded an accuracy, sensitivity, and specificity lower than 55%. Data Conclusion The GOIA‐sLASER sequence with an external phased‐array coil allows for fast assessment of central gland prostate cancer. The classification offers a promising diagnostic tool for discriminating normal vs. cancer, low‐ vs. high‐risk cancer, and low‐ vs. intermediate‐risk cancer. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1926–1936.

Absolute choline tissue concentration mapping for prostate cancer localization and characterization using 3D 1H MRSI without water‐signal suppression

“…In most cases the choline signal was used as a ratio to other prostate metabolites, such as in the commonly used CSC/C ratio, but also as choline/creatine. 3 , 5 , 8 , 44 More detailed analysis of prostate biopsies identified phosphocholine as a major component responsible for this increase. 42 However, the only report we are aware of on the assessment of the absolute level of choline compounds in prostate tumor tissue measured by in vivo MRS did not detect an increase.…”

“…Moreover, our data suggest that the choline content is associated with cancer‐risk groups. Several studies have shown that choline compound signal ratios are correlated with Gleason score in tumors 3 , 7 , 8 , 44 , 46 , 47 and are complementary to DWI in mpMRI as quantitative indicators of tumor aggressiveness. 3 , 4 , 5 , 44 …”

“…However, as multiple studies have demonstrated that MRSI has added clinical value, 3 , 5 it is worthwhile to investigate acquisition and postprocessing methods that go beyond what is standardly available on commercial MR systems, to overcome these limitations and extend the scope of prostate MRSI, such as improved volume selection by semi‐LASER sequences. 6 , 7 , 8 …”

“…However, as multiple studies have demonstrated that MRSI has added clinical value, 3,5 it is worthwhile to investigate acquisition and postprocessing methods that go beyond what is standardly available on commercial MR systems, to overcome these limitations and extend the scope of prostate MRSI, such as improved volume selection by semi-LASER sequences. [6][7][8] Proton MRSI is commonly performed with suppression of the large signal of water to avoid that it distorts the much smaller signals of metabolites. However, this water signal can be very useful for referencing purposes, such as estimating absolute metabolite values and correcting for artifacts such as line shape or phase deformations of metabolite peaks.…”

Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

“…All evaluations were based on biopsy results. Optimal kernel parameters for RBF-SVM were calculated by a grid search approach [15]. The performance of the SVM classification models was determined by leaveone-out cross-validation.…”